Atmospheric Temperature

https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf

import warnings
warnings.filterwarnings("ignore")

import os
import sys
import folium

import numpy as np

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.colors import get_default_line_colors

sys.path.append("../../../functions")
from data_downloaders import GHCN

Define location and variables of interest

country = 'Palau'
vars_interest = ['TMIN', 'TMAX']

Get Data

df_country = GHCN.get_country_code(country)
print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')

The GHCN code for Palau is PS

df_stations = GHCN.download_stations_info()
df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
print(f'There are {df_country_stations.shape[0]} stations in {country}')

There are 13 stations in Palau

GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
for var in vars_interest:
    globals()[f"dict_{var}"], IDS = GHCN.extract_dict_data_var(GHCND_dir, var, df_country_stations)

Plot Data

map = folium.Map(location=[df_country_stations.iloc[0].Latitude-.25, df_country_stations.iloc[0].Longitude], zoom_start=10)

# Color list
colors = get_default_line_colors()

# Add markers
ids_with_data = df_country_stations[df_country_stations['ID'].isin(np.unique(IDS))]
for i in range(len(ids_with_data)):
    folium.Marker(
        location=[ids_with_data.iloc[i].Latitude, ids_with_data.iloc[i].Longitude],
        popup=ids_with_data.iloc[i]['ID'] + ids_with_data.iloc[i]['Name'],
        icon=folium.DivIcon(
        html=f'<div style="font-size: 25px; color: white; background-color: {colors[i]}; line-height: 1; width: 24px; padding: 0px;">&#9737;</div>'
    )
    ).add_to(map)

map

Make this Notebook Trusted to load map: File -> Trust Notebook

for var in vars_interest:
    
    dict_plot = globals()[f'dict_{var}']
    fig = plot_timeseries_interactive(dict_plot, trendline=False, ylims = [5, 40])

Using Koror Station

Analysis of how much the maximum and minimum temperatures over time are changing.
The analysis of the difference between these 2 variables will allow us to know how the daily variability is being modified

id = 'PSW00040309' # Koror Station
dict_min = GHCN.extract_dict_data_var(GHCND_dir, 'TMIN', df_country_stations.loc[df_country_stations['ID'] == id])
dict_max = GHCN.extract_dict_data_var(GHCND_dir, 'TMAX', df_country_stations.loc[df_country_stations['ID'] == id])

import pandas as pd
st_data = pd.concat([dict_min[0][0]['data'], (dict_max[0][0]['data'])], axis=1).dropna()
st_data['diff'] = st_data['TMAX'] - st_data['TMIN']

st_data = st_data.resample('Y').mean()

st_data.plot(y='diff', title='Annual difference between TMAX and TMIN', color='black', legend=False)

<Axes: title={'center': 'Annual difference between TMAX and TMIN'}, xlabel='DATE'>

dict_plot = [{'data' : st_data, 'var' : 'TMIN', 'ax' : 1, 'label' : 'TMIN'},
        # {'data' : st_data, 'var' : 'TMAX', 'ax' : 1, 'label' : 'TMAX'},
        # {'data' : st_data, 'var' : 'diff', 'ax' : 1, 'label' : 'Difference TMAX - TMIN'}
        ]

dict_plot[0].get('data')

	TMIN	TMAX	diff
DATE
1951-12-31	24.150543	30.924457	6.773913
1952-12-31	23.837978	30.904645	7.066667
1953-12-31	24.088767	31.039726	6.950959
1954-12-31	25.000548	31.084384	6.083836
1955-12-31	23.756986	30.741918	6.984932
...	...	...	...
2020-12-31	25.131752	29.885036	4.753285
2021-12-31	24.745042	28.618414	3.873371
2022-12-31	24.541781	29.917466	5.375685
2023-12-31	25.518440	30.079787	4.561348
2024-12-31	25.841463	30.137979	4.296516

74 rows × 3 columns

dict_plot = [{'data' : st_data, 'var' : 'TMIN', 'ax' : 1, 'label' : 'TMIN'}]
fig = plot_timeseries_interactive(dict_plot, trendline=True)

dict_plot = [{'data' : st_data, 'var' : 'TMAX', 'ax' : 1, 'label' : 'TMAX'}]
fig = plot_timeseries_interactive(dict_plot, trendline=True)

dict_plot = [{'data' : st_data, 'var' : 'diff', 'ax' : 1, 'label' : 'Difference TMAX - TMIN'}]
fig = plot_timeseries_interactive(dict_plot, trendline=True)